The newest TEMUL Toolkit version has now been released! The new version features a documentation website (see link below) with many code examples and walk-throughs. Built upon the excellent Hyperspy and Atomap python packages, the TEMUL Toolkit is designed to allow researchers to understand their atomic resolution data in an intuitive manner. It is open to new contributors and we hope it can be a place for others to publish useful and interesting functions and code. The TEMUL Toolkit can be easily installed with PIP (link below), and contains many different modules for image analysis.
The "polarisation" module allows the user, with the aid of Atomap, to find and plot pico-meter atomic shifts. The "plot_polarisation_vectors" function is extensive, and will give any researcher the necessary tools to view and understand the movement of atomic columns in many atomic resolution images (see link below). Additionally, the module contains several structure tools, such as functions for calculating and plotting lattice strain, atomic plane rotation, c/a ratio, and atomic plane curvature.
The "signal_plotting" module provides functions for fast line intensity profile comparisons between regions in an image and between images. The "signal_processing" module gives the user the ability to interactively filter any 2D image with a double Gaussian filter (see button link below). It also allows one to mask an FFT of an image in an easy manner, similar to that of the GMS software.
More complicated analysis routines are also available through the Model Refiner class, the "simulations" module (based on PyPrismatic) and the "signal_processing" module, such as automatic image filtering, simulation, and quantification. Some of the quantification routines are now improved and implemented in an upcoming Atomap package release! For more information on these quantification and simulation methods, check out the PyCon Dublin 2019 talk below, presented by Eoghan O'Connell.
Several publications using the TEMUL Toolkit are soon to be published. A guided walk-through of the data analysis for each publication will also be made available as interactive, in-browser Jupyter Notebooks.